Coaching portal and methods based on behavioral assessment data

ABSTRACT

An interface portal system that includes a non-transitory computer readable medium having a plurality of stored instructions adapted to generate a coaching portal based on behavioral assessment data, the plurality of instructions including instructions that, when executed, analyze one or more communications between a customer and an agent, wherein the analysis comprises instructions that, when executed, apply a linguistic-based psychological behavioral model to separated voice data for the customer, the agent, or both, from each communication by analyzing behavioral characteristics of the customer, the agent, or both, based on the one or more communications; instructions that, when executed, identify one or more customer-agent interaction events based on the analyzed behavioral characteristics; and instructions that, when executed, display a time-based graphic representation across a selected time interval based on one or more communications. Methods of providing coaching assessment based on behavioral assessment data are also included.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/230,032, filed Aug. 5, 2016, now allowed, which is a continuation of U.S. patent application Ser. No. 14/960,194, filed Dec. 4, 2015, now U.S. Pat. No. 9,432,511, which is a continuation of U.S. patent application Ser. No. 12/079,828, filed Mar. 28, 2008, now U.S. Pat. No. 9,225,841, which claims priority to U.S. Provisional Patent Application No. 60/921,109, filed Mar. 30, 2007, and which is also a continuation-in-part of U.S. patent application Ser. No. 11/365,432, filed Mar. 1, 2006, now U.S. Pat. No. 8,094,790, which is a continuation-in-part of U.S. patent application Ser. No. 11/131,844, filed May 18, 2005, now abandoned, the disclosure of each of which is hereby incorporated herein by reference thereto.

TECHNICAL FIELD

The invention relates to a method and system for analyzing an electronic communication, more particularly, to analyzing a communication between a customer and a contact center by applying a psychological behavioral model thereto to obtain behavioral assessment data and to calculate a customer satisfaction score based on the assessment data.

BACKGROUND OF THE INVENTION

It is known to utilize telephone call centers to facilitate the receipt, response and routing of incoming telephone calls relating to customer service, retention, and sales. Generally, a customer is in contact with a customer service representative (“CSR”) or call center agent who is responsible for answering the customer's inquiries and/or directing the customer to the appropriate individual, department, information source, or service as required to satisfy the customer's needs.

It is also well known to monitor calls between a customer and a call center agent. Accordingly, call centers typically employ individuals responsible for listening to the conversation between the customer and the agent. Many companies have in-house call centers to respond to customers' complaints and inquiries. In many case, however, it has been found to be cost effective for a company to hire third party telephone call centers to handle such inquiries. As such, the call centers may be located thousands of miles away from the actual sought manufacturer or individual. This often results in use of inconsistent and subjective methods of monitoring, training and evaluating call center agents. These methods also may vary widely from call center to call center.

While monitoring such calls may occur in real time, it is often more efficient and useful to record the call for later review. Information gathered from the calls is typically used to monitor the performance of the call center agents to identify possible training needs. Based on the review and analysis of the conversation, a monitor will make suggestions or recommendations to improve the quality of the customer interaction. Improvements typically relate to an agent's performance, which generally leads to higher customer satisfaction.

Accordingly, there is a need in customer relationship management (“CRM”) for an objective tool useful in improving the quality of customer interactions with agents and ultimately customer relationships. In particular, a need exists for an objective monitoring and analysis tool which provides information about a customer's perception of an interaction during a call. In the past, post-call data collection methods have been used to survey callers for feedback. This feedback may be subsequently used by a supervisor or trainer to evaluate an agent. Although such surveys have enjoyed some degree of success, their usefulness is directly tied to a customer's willingness to provide post-call data.

More “passive” methods have also been employed to collect data relating to a customer's in-call experience. For example, U.S. Pat. No. 6,724,887 to Eilbacher et al. is directed to a method and system for analyzing a customer communication with a contact center. According to Eilbacher, a contact center may include a monitoring system which records customer communications and a customer experience analyzing unit which reviews the customer communications. The customer experience analyzing unit identifies at least one parameter of the customer communications and automatically determines whether the identified parameter of the customer communications indicates a negative or unsatisfactory experience. According to Eilbacher, a stress analysis may be performed on audio telephone calls to determine a stress parameter by processing the audio portions of the telephone calls. From this, it can then be determined whether the customer experience of the caller was satisfactory or unsatisfactory.

While the method of Eilbacher provides some benefit with respect to reaching an ultimate conclusion as to whether a customer's experience was satisfactory or unsatisfactory, the method provides little insight into the reasons for an experiential outcome. As such, the method of Eilbacher provides only limited value in training agents for future customer communications. Accordingly, there exists a need for a system that analyzes the underlying behavioral characteristics of a customer and agent so that data relating to these behavioral characteristics can be used for subsequent analysis and training.

Systems such as stress analysis systems, spectral analysis models and word-spotting models also exist for determining certain characteristics of audible sounds associated with a communication. For example, systems such as those disclosed in U.S. Pat. No. 6,480,826 to Pertrushin provide a system and method for determining emotions in a voice signal. However, like Eilbacher, these systems also provide only limited value in training customer service agents for future customer interactions. Moreover, such methods have limited statistical accuracy in determining stimuli for events occurring throughout an interaction.

It is well known that certain psychological behavioral models have been developed as tools to evaluate and understand how and/or why one person or a group of people interacts with another person or group of people. The Process Communication Model™ (“PCM”) developed by Dr. Taibi Kahler is an example of one such behavioral model. Specifically, PCM presupposes that all people fall primarily into one of six basic personality types: Reactor, Workaholic, Persister, Dreamer, Rebel and Promoter. Although each person is one of these six types, all people have parts of all six types within them arranged like a “six-tier configuration.” Each of the six types learns differently, is motivated differently, communicates differently, and has a different sequence of negative behaviors in which they engage when they are in distress. Importantly each PCM personality type responds positively or negatively to communications that include tones or messages commonly associated with another of the PCM personality types. Thus, an understanding of a communicant's PCM personality type offers guidance as to an appropriate responsive tone or message. There exists a need for a system and method that analyzes the underlying behavioral characteristics of a customer and agent communication by automatically applying a psychological behavioral model such as, for example PCM, to the communication.

Devices and software for recording and logging calls to a call center are well known. However, application of word-spotting analytical tools to recorded audio communications can pose problems. Devices and software that convert recorded or unrecorded audio signals to text files are also known the art. But, translation of audio signals to text files often results in lost voice data due to necessary conditioning and/or compression of the audio signal. Accordingly, a need also exists to provide a system that allows a contact center to capture audio signals and telephony events with sufficient clarity to accurately apply a linguistic-based psychological behavioral analytic tool to a telephonic communication.

Moreover, it is generally known to store and/or record telephone calls for later review. However, due to the high volume of recorded calls within, for example, a call center, it is difficult to find meaningful examples of calls to drive improvements at a later time. Call centers typically hire individuals to manually sift or sort through calls to find good examples. Typically, an individual must typically manually listen to a call and manually rank or categorize the call.

A need exists, therefore, for a systematic way to automatically categorize and sort calls so as to search and find or otherwise navigate to call examples that may be utilized to drive improvements. Further, a need exists for a method and system for inputting multiple metrics for determining call examples, and navigation to call examples based on these metrics.

The present invention is provided to solve the problems discussed above and other problems, and to provide advantages and aspects not previously provided. A full discussion of the features and advantages of the present invention is deferred to the following detailed description, which proceeds with reference to the accompanying drawings.

SUMMARY OF THE INVENTION

A first aspect of the disclosure encompasses an embodiment including an interface portal system that includes a non-transitory computer readable medium comprising a plurality of instructions stored therein adapted to generate a coaching portal based on behavioral assessment data, the plurality of instructions including instructions that, when executed, analyze one or more communications between a customer and an agent, wherein the analysis includes instructions that, when executed, apply a linguistic-based psychological behavioral model to separated voice data for the customer, the agent, or both, from each communication by analyzing behavioral characteristics of the customer, the agent, or both, based on the one or more communications, instructions that, when executed, identify one or more customer-agent interaction events based on the analyzed behavioral characteristics, and instructions that, when executed, display a time-based graphic representation including a plurality of the identified customer-agent interaction events across a selected time interval based on one or more communications.

A second aspect of the disclosure encompasses an embodiment including a method of providing coaching assessment based on behavioral assessment data across one or more recorded communications, which includes analyzing, by a server, one or more communications between a customer and an agent, wherein the analysis comprises instructions that, when executed, apply a linguistic-based psychological behavioral model to separated voice data for the customer, the agent, or both, from each communication by analyzing behavioral characteristics of the customer, the agent, or both, based on the one or more communications, identifying one or more customer-agent interaction events based on the analyzed behavioral characteristics, and displaying a time-based graphic representation including a plurality of the identified customer-agent interaction events across a selected time interval based on one or more communications.

In a third aspect of the disclosure, the disclosure encompasses a non-transitory computer readable medium including a plurality of instructions stored therein adapted to generate a customer satisfaction score based on behavioral assessment data, the plurality of instructions including instructions that, when executed, analyze one or more communications between a customer and an agent, wherein the analysis includes instructions that, when executed, apply a linguistic-based psychological behavioral model to each communication to determine a personality type of the customer by analyzing behavioral characteristics of the customer based on the one or more communications; instructions that, when executed, select at least one filter criterion which includes a customer, an agent, a team, or a call type, instructions that, when executed, calculate a customer satisfaction score using the at least one selected filter criterion across a selected time interval and based on one or more communications, and instructions that, when executed, display a report including the calculated customer satisfaction score to a user that matches the at least one selected filter criterion for the selected time interval.

In a fourth aspect of the disclosure, the disclosure encompasses a method of generating a customer satisfaction score based on behavioral assessment data across one or more recorded communications that includes by analyzing one or more communications between a customer and an agent, wherein the analyzing includes applying a linguistic-based psychological behavioral model to each communication to determine a personality type of the customer by analyzing behavioral characteristics of the customer based on the one or more communications, selecting at least one filter criterion which includes a customer, an agent, a team, or a call type, calculating a customer satisfaction score using the at least one selected filter criterion across a selected time interval and based on one or more communications, and displaying a report including the calculated customer satisfaction score to a user that matches the at least one selected filter criterion for the selected time interval.

In a fifth aspect, the disclosure encompasses a system configured to generate a customer satisfaction score based on behavioral assessment data across one or more recorded communications, which includes a non-transitory computer readable medium operably coupled to one or more processors, the non-transitory computer readable medium including a plurality of instructions stored therein that are accessible to, and executable by, the one or more processors, wherein the plurality of instructions includes instructions that, when executed, analyze one or more communications between a customer and an agent, wherein the analysis includes instructions that, when executed, apply a linguistic-based psychological behavioral model to each communication to determine a personality type of the customer by analyzing behavioral characteristics of the customer based on the one or more communications, instructions that, when executed, select at least one filter criterion which includes a customer, an agent, a team, or a call type, instructions that, when executed, calculate a customer satisfaction score using the at least one selected filter criterion across a selected time interval and based on one or more communications, and instructions that, when executed, display a report including the calculated customer satisfaction score to a user that matches the at least one selected filter criterion for the selected time interval.

According to a further aspect of the present invention, a method for analyzing a communication between a customer and a contact center is provided. According to the method, a communication is separated into at least first constituent voice data and second constituent voice data. One of the first and second constituent voice data is analyzed by mining the voice data and applying a predetermined linguistic-based psychological behavioral model to one of the separated first and second constituent voice data. Behavioral assessment data is generated which corresponds to the analyzed voice data.

According to another aspect of the present invention, the communication is received in digital format. The step of separating the communication into at least a first and second constituent voice data comprises the steps of identifying a communication protocol associated with the communication, and recording the communication to a first electronic data file. The first electronic data file is comprised of a first and second audio track. The first constituent voice data is automatically recorded on the first audio track based on the identified communication protocol, and the second constituent voice data is automatically recorded on the second audio track based on the identified communication protocol. At least one of the first and second constituent voice data recorded on the corresponding first and second track is separated from the first electronic data file. It is also contemplated that two first data files can be created, wherein the first audio track is recorded to one of the first data file and the second audio track is recorded to the other first data file.

According to another aspect of the present invention, the method described above further comprises the step of generating a text file before the analyzing step. The text file includes a textual translation of either or both of the first and second constituent voice data. The analysis is then performed on the translated constituent voice data in the text file.

According to another aspect of the present invention, the predetermined linguistic-based psychological behavioral model is adapted to assess distress levels in a communication. Accordingly, the method further comprises the step of generating distress assessment data corresponding to the analyzed second constituent voice data.

According to yet another aspect of the present invention event data is generated. The event data corresponds to at least one identifying indicia and time interval. The event data includes at least one of behavioral assessment data or distress assessment data. It is also contemplated that both behavioral assessment data and distress assessment data are included in the event data.

According to still another aspect of the present invention, the communication is one of a plurality of communications. Accordingly, the method further comprises the step of categorizing the communication as one of a plurality of call types and/or customer categories. The communication to be analyzed is selected from the plurality of communications based upon the call type and/or the customer category in which the communication is categorized.

According to still another aspect of the present invention, a responsive communication to the communication is automatically generated based on the event data generated as result of the analysis.

According to another aspect of the present invention, a computer program for analyzing a communication is provided. The computer program is embodied on a computer readable storage medium adapted to control a computer. The computer program comprises a plurality of code segments for performing the analysis of the communication. In particular, a code segment separates a communication into first constituent voice data and second constituent voice data. The computer program also has a code segment that analyzes one of the first and second voice data by applying a predetermined psychological behavioral model to one of the separated first and second constituent voice data. And, a code segment is provided for generating behavioral assessment data corresponding to the analyzed constituent voice data.

According to yet another aspect of the present invention, the computer program comprises a code segment for receiving a communication in digital format. The communication is comprised of a first constituent voice data and a second constituent voice data. A code segment identifies a communication protocol associated with the communication. A code segment is provided for separating the first and second constituent voice data one from the other by recording the communication in stereo format to a first electronic data file. The first electronic data file includes a first and second audio track. The first constituent voice data is automatically recorded on the first audio track based on the identified communication protocol, and the second constituent voice data is automatically recorded on the second audio track based on the identified communication protocol.

A code segment applies a non-linguistic based analytic tool to the separated first constituent voice data and generates phone event data corresponding to the analyzed first constituent voice data. A code segment is provided for translating the first constituent voice data into text format and storing the translated first voice data in a first text file. A code segment analyzes the first text file by mining the text file and applying a predetermined linguistic-based psychological behavioral model to the text file. Either or both of behavioral assessment data and distress assessment data corresponding to the analyzed first voice data is generated therefrom.

According to another aspect of the present invention, the above analysis is performed on the second constituent voice data. Additionally, a code segment is provided for generating call assessment data by comparatively analyzing the behavioral assessment data and distress assessment data corresponding to the analyzed first voice data and the behavioral assessment data and distress assessment data corresponding to the analyzed second voice data. The computer program has a code segment for outputting event data which is comprised of call assessment data corresponding to at least one identifying indicia and at least one predetermined time interval.

According to still another aspect of the present invention, a method for analyzing an electronic communication is provided. The method comprises the step of receiving an electronic communication in digital format. The electronic communication includes communication data. The communication data is analyzed by applying a predetermined linguistic-based psychological behavioral model thereto. Behavioral assessment data corresponding to the analyzed communication data is generated therefrom.

The method described can be embodied in a computer program stored on a computer readable media. The a computer program would include code segments or routines to enable all of the functional aspects of the interface described or shown herein.

According to another aspect of the invention, a computer program for training a customer service representative by analyzing a communication between a customer and a contact center is provided. A code segment selects at least one identifying criteria. A code segment identifies a pre-recorded first communication corresponding to the selected identifying criteria. The first communication has first event data associated therewith. A code segment generates coaching assessment data corresponding to the identified pre-recorded first communication. A code segment identifies a pre-recorded second communication corresponding to the selected identifying criteria. The second communication has second event data associated therewith. A code segment compares the identified pre-recorded second communication to the identified first communication within the coaching assessment data. A code segment generates a notification based on the comparison of the identified pre-recorded second communication with the identified first communication within the coaching assessment data.

According to yet another aspect of the present invention, a code segment generates a first performance score for the coaching assessment. A code segment generates a second performance score for the pre-recorded second communication. The notification is generated based on a comparison of first performance score with the second performance score.

According to still another aspect of the present invention, a code segment identifies a plurality of pre-recorded first communications based on at least one identifying criteria. Each of the first communications has first event data associated therewith. A code segment for identifies a plurality of pre-recorded second communications based on at least one identifying criteria. Each of the second communications having second event data associated therewith. A code segment generates a first performance score for each of the plurality of prerecorded first communications and a code segment generates a second performance score for each of the plurality of prerecorded second communications. A code segment generates a notification if a predetermined number of second performance scores are at least one of less than a predetermined threshold of the first performance scores and greater than a predetermined threshold of the first performance scores.

According to another aspect of the present invention, a computer program for training a customer service representative by analyzing a communication between a customer and a contact center is provided. A code segment selects at least one identifying criteria. A code segment identifies a pre-recorded first communications corresponding to the selected identifying criteria. The first communication having first event data associated therewith. A code segment generates coaching assessment data corresponding to the identified pre-recorded first communication. A code segment compares the identified first communication within the coaching assessment data with a predetermined identifying criteria value threshold. A code segment generates a notification based on the comparison of the identified first communication with the coaching assessment data with a predetermined identifying criteria value threshold.

According to yet another aspect of the invention, a code segment generates a first performance score for the coaching assessment data. A code segment generates a second performance for the identifying criteria value threshold. A code segment generates a notification. The notification is generated based on a comparison of first performance score and the second performance score.

According to another aspect of the invention, a code segment identifies a plurality of pre-recorded first communications based on at least one identifying criteria. Each of the first communications having first event data associated therewith. A code segment generates a first performance score for each of the plurality of prerecorded first communications based on the at least one identifying criteria. A code segment generates a second performance score based on the identifying criteria value threshold. A code segment generates a notification. The notification is generated if a predetermined threshold of first performance scores are at least one of less than the second performance score and greater than the second performance scores.

According to still another aspect of the present invention, the computer program further comprises a code segment for generating a graphical user interface (“GUI”). The GUI is adapted to display a first field for enabling identification of customer interaction event information on a display. The customer interaction event information includes call assessment data based on the psychological behavioral model applied to the analyzed constituent voice data of each customer interaction event. The computer program also includes a code segment for receiving input from a user for identifying at least a first customer interaction event. A code segment is also provided for displaying the customer interaction event information for the first customer interaction event.

According to one aspect of the present invention, the GUI enables a user of the system to locate one or more caller interaction events (i.e., calls between a caller and the call center), and to display information relating to the event. In particular, the graphical user interface provides a visual field showing the results of the psychological behavioral model that was applied to a separated voice data from the caller interaction event. Moreover, the interface can include a link to an audio file of a selected caller interaction event, and a visual representation that tracks the portion of the caller interaction that is currently heard as the audio file is being played.

According to one aspect of the invention, the graphical user interface is incorporated in a system for identifying one or more caller interaction events and displaying a psychological behavioral model applied to a separated voice data of a customer interaction event. The system comprises a computer coupled to a display and to a database of caller interaction event information. The caller interaction event information includes data resulting from application of a psychological behavioral model to a first voice data separated from an audio wave form of a caller interaction event. Additionally, the caller event information can also include additional information concerning each call, such as statistical data relating to the caller interaction event (e.g., time, date and length of call, caller identification, agent identification, hold times, transfers, etc.), and a recording of the caller interaction event.

The system also includes a processor, either at the user's computer or at another computer, such as a central server available over a network connection, for generating a graphical user interface on the display. The graphical user interface comprises a selection visual field for enabling user input of caller interaction event parameters for selection of at least a first caller interaction event and/or a plurality of caller interaction events. The caller interaction event parameters can include one or more caller interaction event identifying characteristic. These characteristics can include, for example, the caller's name or other identification information, a date range, the agent's name, the call center identification, a supervisor identifier, etc. For example, the graphical user interface can enable a user to select all caller interaction events for a particular caller; or all calls handled by a particular agent. Both examples can be narrowed to cover a specified time period or interval. The interface will display a selected caller interaction event field which provides identification of caller interaction events corresponding to the user input of caller interaction event parameters.

The graphical user interface also includes a conversation visual field for displaying a time-based representation of characteristics of the caller interaction event(s) based on the psychological behavioral model. These characteristics were generated by the application of a psychological behavioral model to a first voice data separated from an audio wave form of a caller interaction event which is stored as part of the caller interaction event information.

The conversation visual field can include a visual link to an audio file of the caller interaction event(s). Additionally, it may also include a graphical representation of the progress of the first caller interaction event that corresponds to a portion of the audio file being played. For example, the interface may show a line representing the call and a moving pointer marking the position on the line corresponding to the portion of the event being played. Additionally, the time-based representation of characteristics of the caller interaction event can include graphical or visual characteristic elements which are also displayed in the conversation visual field. Moreover, the characteristic elements are located, or have pointers to, specific locations of the graphical representation of the progress of the event corresponding to where the element was generated by the analysis.

The graphical user interface further includes a call statistics visual field selectable by a user for displaying statistics pertaining to the caller interaction events. The statistics in the call statistics visual field can include, for example: call duration, caller talk time, agent talk time, a caller satisfaction score, an indication of the number of silences greater than a predetermined time period, and an agent satisfaction score.

The graphical user interface can also include a number of other visual fields. For example, the graphical user interface can include a caller satisfaction report field for displaying one or more caller satisfaction reports, or a user note field for enabling a user of the system to place a note with the first caller interaction event.

In accordance with another embodiment of the invention, a method for identifying one or more caller interaction events and displaying an analysis of a psychological behavioral model applied to a separated voice data from the caller interaction event comprises providing a graphical user interface for displaying a first field for enabling identification of caller interaction event information on a display, the caller interaction event information including analysis data based on a psychological behavioral model applied to a first separated voice data of each caller interaction event; receiving input from a user for identifying at least a first caller interaction event; and, displaying the caller interaction event information for the first caller interaction event on the display. The step of receiving input from a user can include receiving at least one or more of a caller identifier, a call center identifier, an agent identifier, a supervisor identifier, and a date range.

The step of displaying the caller interaction event information for the first caller interaction event on the display can include displaying a time-based representation of characteristics of the first caller interaction event based on the psychological behavioral model. The method can also include providing an audio file of the first caller interaction event. In this regard, the displaying of the time-based representation of characteristics of the first caller event based on the psychological behavioral model can include displaying a graphical representation of the progress of the first caller interaction event that corresponds to a portion of the audio file being played.

The graphical user interface can be generated by a user's local computer, or from a remote server coupled to the user's computer via a network connection. In this latter instance, the method can further include creating a web page containing the graphical user interface that is downloadable to a user's computer, and downloading the page via the network connection.

The method can include providing other visual fields for enabling other functions of the system. For example, the method can include providing a field in the graphical user interface for enabling a user to place a note with the information for the first caller interaction event.

The graphical user interface described can be embodied in a computer program stored on a computer readable media. The a computer program would include code segments or routines to enable all of the functional aspects of the interface described or shown herein.

Other features and advantages of the invention will be apparent from the following specification taken in conjunction with the following drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

To understand the present invention, it will now be described by way of example, with reference to the accompanying drawings in which:

FIG. 1 is a block diagram of call center;

FIG. 2 is a block diagram of the recording engine and behavioral analysis engine according to the present invention;

FIG. 3 is a block diagram of a computer used in connection with the present invention;

FIG. 4 is a flow chart illustrating the process of analyzing a telephonic communication in accordance with the present invention;

FIG. 5 is a flow chart illustrating the process of analyzing a telephonic communication in accordance with the present invention;

FIG. 6 is a flow chart illustrating the process of analyzing a telephonic communication in accordance with the present invention;

FIG. 7 is a block diagram of a telephonic communication system according to the present invention;

FIG. 8 is a block diagram of a telephonic communication system according to the present invention;

FIG. 9 is a block diagram of a telephonic communication system with a multi-port PSTN module according to the present invention;

FIG. 10 is a flow chart illustrating the process of recording and separating a telephonic communication in accordance with the present invention;

FIG. 11 is a flow chart illustrating the process of recording and separating a telephonic communication in accordance with the present invention;

FIG. 12 is a flow chart illustrating the process of analyzing separated constituent voice data of a telephonic communication in accordance with the present invention;

FIG. 13 is a flow chart illustrating the process of analyzing separated constituent voice data of a telephonic communication in accordance with the present invention;

FIG. 14-32 are graphical user interface screens of the resultant output from the process of analyzing voice data of a telephonic communication in accordance with the present invention;

FIG. 33 is a flow chart illustrating the process the training the call center agent by analyzing a telephonic communication; and, FIG. 34-36 are graphical user interface screens of the resultant output from the process of analyzing voice data of a telephonic communication in accordance with the present invention.

DETAILED DESCRIPTION

While this invention is susceptible of embodiments in many different forms, there is shown in the drawings and will herein be described in detail preferred embodiments of the invention with the understanding that the present disclosure is to be considered as an exemplification of the principles of the invention and is not intended to limit the broad aspect of the invention to the embodiments illustrated.

Referring to FIGS. 1-32, a method and system for analyzing an electronic communication between a customer and a contact center is provided. A “contact center” as used herein can include any facility or system server suitable for receiving and recording electronic communications from customers. Such communications can include, for example, telephone calls, facsimile transmissions, e-mails, web interactions, voice over IP (“VoIP”) and video. It is contemplated that these communications may be transmitted by and through any type of telecommunication device and over any medium suitable for carrying data. For example, the communications may be transmitted by or through telephone lines, cable or wireless communications. As shown in FIG. 1, the contact center 10 of the present invention is adapted to receive and record varying electronic communications 11 and data formats that represent an interaction that may occur between a customer (or caller) 7 and a contact center agent 9 during fulfillment of a customer/agent transaction.

As shown in FIG. 2, the present method and system for analyzing an electronic communication between a customer 7 and a contact center 10 comprises a recording engine 2 and an behavioral analysis engine 3. As will be described in further detail, an audio communication signal is recorded, separated into constituent audio data, and analyzed in accordance with the methods described below. It is contemplated that the method for analyzing an electronic communication between a customer 7 and a contact center 10 of the present invention can be implemented by a computer program. Now is described in more specific terms, the computer hardware associated with operating the computer program that may be used in connection with the present invention.

Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of the embodiments of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.

FIG. 3 is a block diagram of a computer or server 12. For purposes of understanding the hardware as described herein, the terms “computer” and “server” have identical meanings and are interchangeably used. Computer 12 includes control system 14. The control system 14 of the invention can be implemented in software (e.g., firmware), hardware, or a combination thereof. In the currently contemplated best mode, the control system 14 is implemented in software, as an executable program, and is executed by one or more special or general purpose digital computer(s), such as a personal computer (PC; IBM-compatible, Apple-compatible, or otherwise), personal digital assistant, workstation, minicomputer, or mainframe computer. An example of a general purpose computer that can implement the control system 14 of the present invention is shown in FIG. 3. The control system 14 may reside in, or have portions residing in, any computer such as, but not limited to, a general purpose personal computer. Therefore, computer 12 of FIG. 3 may be representative of any computer in which the control system 14 resides or partially resides.

Generally, in terms of hardware architecture, as shown in FIG. 3, the computer 12 includes a processor 16, memory 18, and one or more input and/or output (I/O) devices 20 (or peripherals) that are communicatively coupled via a local interface 22. The local interface 22 can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface 22 may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate communications among the other computer components.

The processor 16 is a hardware device for executing software, particularly software stored in memory 18. The processor 16 can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the computer 12, a semiconductor based microprocessor (in the form of a microchip or chip set), a macroprocessor, or generally any device for executing software instructions. Examples of suitable commercially available microprocessors are as follows: a PA-RISC series microprocessor from Hewlett-Packard Company, an 80.times.8 or Pentium series microprocessor from Intel Corporation, a PowerPC microprocessor from IBM, a Sparc microprocessor from Sun Microsystems, Inc., or a 8xxx series microprocessor from Motorola Corporation.

The memory 18 can include any one or a combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.). Moreover, memory 18 may incorporate electronic, magnetic, optical, and/or other types of storage media. The memory 18 can have a distributed architecture where various components are situated remote from one another, but can be accessed by the processor 16.

The software in memory 18 may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. In the example of FIG. 3, the software in the memory 18 includes the control system 14 in accordance with the present invention and a suitable operating system (O/S) 24. A non-exhaustive list of examples of suitable commercially available operating systems 24 is as follows: (a) a Windows operating system available from Microsoft Corporation; (b) a Netware operating system available from Novell, Inc.; (c) a Macintosh operating system available from Apple Computer, Inc.; (d) a UNIX operating system, which is available for purchase from many vendors, such as the Hewlett-Packard Company, Sun Microsystems, Inc., and AT&T Corporation; (e) a LINUX operating system, which is freeware that is readily available on the Internet; (f) a run time Vxworks operating system from WindRiver Systems, Inc.; or (g) an appliance-based operating system, such as that implemented in handheld computers or personal digital assistants (PDAs) (e.g., PalmOS available from Palm Computing, Inc., and Windows CE available from Microsoft Corporation). The operating system 24 essentially controls the execution of other computer programs, such as the control system 14, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services.

The control system 14 may be a source program, executable program (object code), script, or any other entity comprising a set of instructions to be performed. When a source program, the program needs to be translated via a compiler, assembler, interpreter, or the like, which may or may not be included within the memory 18, so as to operate properly in connection with the O/S 24. Furthermore, the control system 14 can be written as (a) an object oriented programming language, which has classes of data and methods, or (b) a procedure programming language, which has routines, subroutines, and/or functions, for example but not limited to, C, C++, Pascal, Basic, Fortran, Cobol, Perl, Java, and Ada. In one embodiment, the control system 14 is written in C++. The I/O devices 20 may include input devices, for example but not limited to, a keyboard, mouse, scanner, microphone, touch screens, interfaces for various medical devices, bar code readers, stylus, laser readers, radio-frequency device readers, etc. Furthermore, the I/O devices 20 may also include output devices, for example but not limited to, a printer, bar code printers, displays, etc. Finally, the I/O devices 20 may further include devices that communicate both inputs and outputs, for instance but not limited to, a modulator/demodulator (modem; for accessing another device, system, or network), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, etc.

If the computer 12 is a PC, workstation, PDA, or the like, the software in the memory 18 may further include a basic input output system (BIOS) (not shown in FIG. 3). The BIOS is a set of software routines that initialize and test hardware at startup, start the O/S 24, and support the transfer of data among the hardware devices. The BIOS is stored in ROM so that the BIOS can be executed when the computer 12 is activated.

When the computer 12 is in operation, the processor 16 is configured to execute software stored within the memory 18, to communicate data to and from the memory 18, and to generally control operations of the computer 12 pursuant to the software. The control system 14 and the O/S 24, in whole or in part, but typically the latter, are read by the processor 16, perhaps buffered within the processor 16, and then executed.

When the control system 14 is implemented in software, as is shown in FIG. 3, it should be noted that the control system 14 can be stored on any computer readable medium for use by or in connection with any computer related system or method. In the context of this document, a “computer-readable medium” can be any means that can store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer readable medium can be for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM) (electronic), a read-only memory (ROM) (electronic), an erasable programmable read-only memory (EPROM, EEPROM, or Flash memory) (electronic), an optical fiber (optical), and a portable compact disc read-only memory (CDROM) (optical). The control system 14 can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.

In another embodiment, where the control system 14 is implemented in hardware, the control system 14 can be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

FIG. 4 illustrates the general flow of one embodiment of the method of analyzing voice data according to the present invention. As shown, an uncompressed digital stereo audio waveform of a conversation between a customer and a call center agent is recorded and separated into customer voice data and call center agent voice data 26. The voice data associated with the audio waveform is then mined and analyzed using multi-stage linguistic and non-linguistic analytic tools 28. The analysis data is stored 30 and can be accessed by a user 31 (e.g., CSR supervisor) through an interface portal 32 for subsequent review 32. The digital stereo audio waveform is compressed 34 and stored 36 in an audio file which is held on a media server 38 for subsequent access through the interface portal 32.

The method of the present invention is configured to postpone audio compression until analysis of the audio data is complete. This delay allows the system to apply the analytic tools to a truer and clearer hi-fidelity signal. The system employed in connection with the present invention also minimizes audio distortion, increases fidelity, eliminates gain control and requires no additional filtering of the signal.

As shown in FIG. 6, according to one embodiment, the method of the present invention more specifically comprises the step of separating a telephonic communication 2 into first constituent voice data and second constituent voice data 40. One of the first or second constituent voice data is then separately analyzed by applying a predetermined psychological behavioral model thereto 42 to generate behavioral assessment data 44. In one embodiment discussed in detail below, linguistic-based behavioral models are adapted to assess behavior based on behavioral signifiers within a communications are employed. It is contemplated that one or more psychological behavioral models may be applied to the voice data to generate behavioral assessment data therefrom.

The telephonic communication 2 being analyzed can be one of numerous calls stored within a contact center server 12, or communicated to a contact center during a given time period. Accordingly, the present method contemplates that the telephonic communication 2 being subjected to analysis is selected from the plurality of telephonic communications. The selection criteria for determining which communication should be analyzed may vary. For example, the communications coming into a contact center can be automatically categorized into a plurality of call types using an appropriate algorithm. For example, the system may employ a word-spotting algorithm that categorizes communications 2 into particular types or categories based on words used in the communication. In one embodiment, each communication 2 is automatically categorized as a service call type (e.g., a caller requesting assistance for servicing a previously purchased product), a retention call type (e.g., a caller expressing indignation, or having a significant life change event), or a sales call type (e.g., a caller purchasing an item offered by a seller). In one scenario, it may be desirable to analyze all of the “sales call type” communications received by a contact center during a predetermined time frame. In that case, the user would analyze each of the sales call type communications from that time period by applying the predetermined psychological behavioral model to each such communication.

Alternatively, the communications 2 may be grouped according to customer categories, and the user may desire to analyze the communications 2 between the call center and communicants within a particular customer category. For example, it may be desirable for a user to perform an analysis only of a “platinum customers” category, consisting of high end investors, or a “high volume distributors” category comprised of a user's best distributors.

In one embodiment the telephonic communication 2 is telephone call in which a telephonic signal is transmitted. As many be seen in FIGS. 7 and 8, a customer sending a telephonic signal may access a contact center 10 through the public switched telephone network (PSTN) 203 and an automatic call distribution system (PBX/ACD) 205 directs the communication to one of a plurality of agent work stations 211, 213. Each agent work station 211, 213 includes, for example, a computer 215 and a telephone 213.

When analyzing voice data, it is preferable to work from a true and clear hi-fidelity signal. This is true both in instances in which the voice data is being translated into a text format for analysis using a linguistic-based psychological behavioral model thereto, or in instance in which a linguistic-based psychological behavioral model is being applied directly to an audio waveform, audio stream or file containing voice data.

FIG. 7 illustrates a telephonic communication system 201, such as a distributed private branch exchange (PBX), having a public switched telephone network (PSTN) 203 connected to the PBX through a PBX switch 205.

The PBX switch 205 provides an interface between the PSTN 203 and a local network. Preferably, the interface is controlled by software stored on a telephony server 207 coupled to the PBX switch 205. The PBX switch 205, using interface software, connects trunk and line station interfaces of the public switch telephone network 203 to stations of a local network or other peripheral devices contemplated by one skilled in the art. Further, in another embodiment, the PBX switch may be integrated within telephony server 207. The stations may include various types of communication devices connected to the network, including the telephony server 207, a recording server 209, telephone stations 211, and client personal computers 213 equipped with telephone stations 215. The local network may further include fax machines and modems.

Generally, in terms of hardware architecture, the telephony server 207 includes a processor, memory, and one or more input and/or output (I/O) devices (or peripherals) that are communicatively coupled via a local interface. The processor can be any custom-made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the telephony server 207, a semiconductor based microprocessor (in the form of a microchip or chip set), a macroprocessor, or generally any device for executing software instructions. The memory of the telephony server 207 can include any one or a combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.). The telephony server 207 may further include a keyboard and a mouse for control purposes, and an attached graphic monitor for observation of software operation.

The telephony server 207 incorporates PBX control software to control the initiation and termination of connections between stations and via outside trunk connections to the PSTN 203. In addition, the software may monitor the status of all telephone stations 211 in real-time on the network and may be capable of responding to telephony events to provide traditional telephone service. This may include the control and generation of the conventional signaling tones such as dial tones, busy tones, ring back tones, as well as the connection and termination of media streams between telephones on the local network. Further, the PBX control software may use a multi-port module 223 and PCs to implement standard PBX functions such as the initiation and termination of telephone calls, either across the network or to outside trunk lines, the ability to put calls on hold, to transfer, park and pick up calls, to conference multiple callers, and to provide caller ID information. Telephony applications such as voice mail and auto attendant may be implemented by application software using the PBX as a network telephony services provider.

Referring to FIG. 9, in one embodiment, the telephony server 207 is equipped with multi-port PSTN module 223 having circuitry and software to implement a trunk interface 217 and a local network interface 219. The PSTN module 223 comprises a control processor 221 to manage the transmission and reception of network messages between the PBX switch 205 and the telephony network server 207. The control processor 221 is also capable of directing network messages between the PBX switch 205, the local network interface 291, the telephony network server 207, and the trunk interface 217. In the one embodiment, the local network uses Transmission Control Protocol/Internet Protocol (TCP/IP). The network messages may contain computer data, telephony transmission supervision, signaling and various media streams, such as audio data and video data. The control processor 221 directs network messages containing computer data from the PBX switch 205 to the telephony network server 207 directly through the multi-port PSTN module 223.

The control processor 221 may include buffer storage and control logic to convert media streams from one format to another, if necessary, between the trunk interface 217 and local network. The trunk interface 217 provides interconnection with the trunk circuits of the PSTN 203. The local network interface 219 provides conventional software and circuitry to enable the telephony server 207 to access the local network. The buffer RAM and control logic implement efficient transfer of media streams between the trunk interface 217, the telephony server 207, the digital signal processor 225, and the local network interface 219.

The trunk interface 217 utilizes conventional telephony trunk transmission supervision and signaling protocols required to interface with the outside trunk circuits from the PSTN 203. The trunk lines carry various types of telephony signals such as transmission supervision and signaling, audio, fax, or modem data to provide plain old telephone service (POTS). In addition, the trunk lines may carry other communication formats such Ti, ISDN or fiber service to provide telephony or multimedia data images, video, text or audio.

The control processor 221 manages real-time telephony event handling pertaining to the telephone trunk line interfaces, including managing the efficient use of digital signal processor resources for the detection of caller ID, DTMF, call progress and other conventional forms of signaling found on trunk lines. The control processor 221 also manages the generation of telephony tones for dialing and other purposes, and controls the connection state, impedance matching, and echo cancellation of individual trunk line interfaces on the multi-port PSTN module 223.

Preferably, conventional PBX signaling is utilized between trunk and station, or station and station, such that data is translated into network messages that convey information relating to real-time telephony events on the network, or instructions to the network adapters of the stations to generate the appropriate signals and behavior to support normal voice communication, or instructions to connect voice media streams using standard connections and signaling protocols. Network messages are sent from the control processor 221 to the telephony server 207 to notify the PBX software in the telephony server 207 of real-time telephony events on the attached trunk lines. Network messages are received from the PBX Switch 205 to implement telephone call supervision and may control the set-up and elimination of media streams for voice transmission.

The local network interface 219 includes conventional circuitry to interface with the local network. The specific circuitry is dependent on the signal protocol utilized in the local network. In one embodiment, the local network may be a local area network (LAN) utilizing IP telephony. IP telephony integrates audio and video stream control with legacy telephony functions and may be supported through the H.323 protocol. H.323 is an International Telecommunication Union-Telecommunications protocol used to provide voice and video services over data networks. H.323 permits users to make point-to-point audio and video phone calls over a local area network. IP telephony systems can be integrated with the public telephone system through a local network interface 219, such as an IP/PBX-PSTN gateway, thereby allowing a user to place telephone calls from an enabled computer. For example, a call from an IP telephony client to a conventional telephone would be routed on the LAN to the IP/PBX-PSTN gateway. The IP/PBX-PSTN gateway translates H.323 protocol to conventional telephone protocol and routes the call over the conventional telephone network to its destination. Conversely, an incoming call from the PSTN 203 is routed to the IP/PBX-PSTN gateway and translates the conventional telephone protocol to H.323 protocol.

As noted above, PBX trunk control messages are transmitted from the telephony server 207 to the control processor 221 of the multi-port PSTN. In contrast, network messages containing media streams of digital representations of real-time voice are transmitted between the trunk interface 217 and local network interface 219 using the digital signal processor 225. The digital signal processor 225 may include buffer storage and control logic. Preferably, the buffer storage and control logic implement a first-in-first-out (FIFO) data buffering scheme for transmitting digital representations of voice audio between the local network to the trunk interface 217. It is noted that the digital signal processor 225 may be integrated with the control processor 221 on a single microprocessor.

The digital signal processor 225 may include a coder/decoder (CODEC) connected to the control processor 221. The CODEC may be a type TCM29c13 integrated circuit made by Texas Instruments, Inc. In one embodiment, the digital signal processor 225 receives an analog or digital voice signal from a station within the network or from the trunk lines of the PSTN 203. The CODEC converts the analog voice signal into in a digital from, such as digital data packets. It should be noted that the CODEC is not used when connection is made to digital lines and devices. From the CODEC, the digital data is transmitted to the digital signal processor 225 where telephone functions take place. The digital data is then passed to the control processor 221 which accumulates the data bytes from the digital signal processor 225. It is preferred that the data bytes are stored in a first-in-first-out (FIFO) memory buffer until there is sufficient data for one data packet to be sent according to the particular network protocol of the local network. The specific number of bytes transmitted per data packet depends on network latency requirements as selected by one of ordinary skill in the art. Once a data packet is created, the data packet is sent to the appropriate destination on the local network through the local network interface 219. Among other information, the data packet contains a source address, a destination address, and audio data. The source address identifies the location the audio data originated from and the destination address identifies the location the audio data is to be sent.

The system permits bidirectional communication by implementing a return path allowing data from the local network, through the local network interface 219, to be sent to the PSTN 203 through the multi-line PSTN trunk interface 217. Data streams from the local network are received by the local network interface 219 and translated from the protocol utilized on the local network to the protocol utilized on the PSTN 203. The conversion of data may be performed as the inverse operation of the conversion described above relating to the IP/PBX-PSTN gateway. The data stream is restored in appropriate form suitable for transmission through to either a connected telephone 211, 215 or an interface trunk 217 of the PSTN module 223, or a digital interface such as a TI line or ISDN. In addition, digital data may be converted to analog data for transmission over the PSTN 203.

Generally, the PBX switch of the present invention may be implemented with hardware or virtually. A hardware PBX has equipment located local to the user of the PBX system. The PBX switch 205 utilized may be a standard PBX manufactured by Avaya, Siemens AG, NEC, Nortel, Toshiba, Fujitsu, Vodavi, Mitel, Ericsson, Panasonic, or InterTel. In contrast, a virtual PBX has equipment located at a central telephone service provider and delivers the PBX as a service over the PSTN 203.

As illustrated in FIG. 1, the system includes a recording server 209 for recording and separating network messages transmitted within the system. The recording server 209 may be connected to a port on the local network, as seen in FIG. 1. Alternatively, the recording server 209 may be connected to the PSTN trunk line as illustrated in FIG. 7. The recording server 209 includes a control system software, such as recording software. The recording software of the invention can be implemented in software (e.g., firmware), hardware, or a combination thereof. In the currently contemplated best mode, the recording software is implemented in software, as an executable program, and is executed by one or more special or general purpose digital computer(s), such as a personal computer (PC; IBM-compatible, Apple-compatible, or otherwise), personal digital assistant, workstation, minicomputer, or mainframe computer. An example of a general purpose computer that can implement the recording software of the present invention is shown in FIG. 3. The recording software may reside in, or have portions residing in, any computer such as, but not limited to, a general purpose personal computer. Therefore, recording server 209 of FIG. 3 may be representative of any type of computer in which the recording software resides or partially resides.

Generally, hardware architecture is the same as that discussed above and shown in FIG. 3. Specifically, the recording server 209 includes a processor, memory, and one or more input and/or output (I/O) devices (or peripherals) that are communicatively coupled via a local interface as previously described. The local interface can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate communications among the other computer components.

As noted above, the recording server 209 incorporates recording software for recording and separating a signal based on the source address and/or destination address of the signal. The method utilized by the recording server 209 depends on the communication protocol utilized on the communication lines to which the recording server 209 is coupled. In the communication system contemplated by the present invention, the signal carrying audio data of a communication between at least two users may be an analog signal or a digital signal in the form of a network message. In one embodiment, the signal is an audio data transmitted according to a signaling protocol, for example the H.323 protocol described above.

An example of a communication between an outside caller and a call center agent utilizing the present system 200 is illustrated in FIG. 10 and described herein. In the embodiment of FIG. 10, when an outside caller reaches the system through the multi-line interface trunk 217, their voice signal is digitized (if needed) in the manner described above, and converted into digital data packets 235 according to the communication protocol utilized on the local network of the system. The data packet 235 comprises a source address identifying the address of the outside caller, a destination address identifying the address of the call center agent, and first constituent audio data comprising at least a portion of the outside caller's voice. The data packet 235 can further comprise routing data identifying how the data packet 235 should be routed through the system and other relevant data. Once the data packet 235 is created, the data packet 235 is sent to the appropriate destination on the local network, such as to a call center agent, through the local network interface 219. The PBX and/or an automatic call distributor (ACD) can determine the initial communication setup, such as the connection state, impedance matching, and echo cancellation, according to predetermined criteria.

Similar to the process described above, when the call center agent speaks, their voice is digitized (if needed) and converted into digital data packet 235 according to the communication protocol utilized on the local network. The data packet 235 comprises a source address identifying the address of the call center agent, a destination address identifying the address of the outside caller, and second constituent audio data comprising at least a portion of the call center agent's voice. The data packet 235 is received by the local network interface 219 and translated from the communication protocol utilized on the local network to the communication protocol utilized on the PSTN 203. The conversion of data can be performed as described above. The data packet 235 is restored in appropriate form suitable for transmission through to either a connected telephone 211, 215 or an interface trunk 217 of the PSTN module 223, or a digital interface such as a Ti line or ISDN. In addition, digital data can be converted to analog data for transmission through the PSTN 203.

The recording server 209 receives either a data packet 235 comprising: the source address identifying the address of the outside caller, a destination address identifying the address of the call center agent, and the first constituent audio data comprising at least a portion of the outside callers voice; or a data packet 235 comprising a source address identifying the address of the call center agent, a destination address identifying the address of the outside caller, and second constituent audio data comprising at least a portion of the customer's agent voice. It is understood by one of ordinary skill in the art that the recording server 209 is programmed to identify the communication protocol utilized by the local network and extract the audio data within the data packet 235. In one embodiment, the recording server 209 can automatically identify the utilized communication protocol from a plurality of communication protocols. The plurality of communication protocols can be stored in local memory or accessed from a remote database.

The recording server 209 comprises recording software to record the communication session between the outside caller and the call center agent in a single data file in a stereo format. The first data file 241 has at least a first audio track 237 and a second audio track 237. Once a telephone connection is established between an outside caller and a call center agent, the recording software creates a first data file 241 to record the communication between the outside caller and the call center agent. It is contemplated that the entire communication session or a portion of the communication session can be recorded.

Upon receiving the data packet 235, the recording server 209 determines whether to record the audio data contained in the data packet 235 in either the first audio track 237 or the second audio track 239 of the first data file 241 as determined by the source address, destination address, and/or the audio data contained within the received data packet 235. Alternatively, two first data files can be created, wherein the first audio track is recorded to the one of the first data file and the second audio track is recorded to the second first data file. In one embodiment, if the data packet 235 comprises a source address identifying the address of the outside caller, a destination address identifying the address of the call center agent, and first constituent audio data, the first constituent audio data is recorded on the first audio track 237 of the first data file 241. Similarly, if the data packet 235 comprises a source address identifying the address of the call center agent, a destination address identifying the address of the outside caller, and second constituent audio data, the second constituent audio data is recorded on the second audio track 239 of the first data file 241. It should be noted the first and second constituent audio data can be a digital or analog audio waveform or a textual translation of the digital or analog waveform. The recording process is repeated until the communication link between the outside caller and call center agent is terminated.

As noted above, the recording server 209 can be connected to the trunk lines of the PSTN 203 as seen in FIG. 8. The PSTN 203 can utilize a different protocol and therefore, the recording server 209 is configured to identify the communication protocol utilized by the PSTN 203, recognize the source and destination address of a signal and extract the audio data from the PSTN 203. The recording server 209 is programmed in a manner as known to one of ordinary skill in the art.

As shown in FIG. 10, once the communication link is terminated, the recording server 209 ends the recording session and stores the single data file having the recorded communication session in memory. After the first data file is stored in memory, the recording server 209 can extract either or both of the first constituent audio data from the first audio track of the first data file or the second constituent audio data from the second audio track of the first data file. In one embodiment, the first constituent audio data extracted from the first audio track is stored in a first constituent data file 243. Similarly, the second constituent audio data extracted from the second audio track can be stored in a second constituent data file 245. The first and second constituent data files 243, 245 can be compressed before being stored in memory. The extracted data can be in the form of a digital or analog audio waveform or can be a textual translation of the first or second constituent audio data. It is contemplated that either or both of the first constituent data file 243 or the second constituent data file 245 can be further analyzed or processed. For example, among other processes and analyses, filtering techniques can be applied to the first constituent data file and/or the second constituent data file. Moreover, event data, such as silence periods or over-talking, can be identified through analysis techniques known to those skilled in the art.

Further, as illustrated in FIG. 10, the first constituent data file 243 and second constituent data file 245 can be merged together into a single second data file 247. The first and second constituent data files can be merged in a stereo format where the first constituent audio data from the first constituent data file 243 is stored on a first audio track of the second data file 247 and the second constituent audio data from the second constituent data file 245 is stored on a second audio track of the second data file 247. Alternatively, the first and second constituent data files can be merged in a mono format where the first constituent audio data from the first constituent data file 243 and the second constituent audio data from the second constituent data file 245 are stored on a first audio track of the second data file 247. Additionally, the first and second constituent audio data can be merged into a document having a textual translation of the audio data. In such a case, identifiers can be associated with each of the merged first and second constituent audio data in order to associate the merged first constituent audio data with the outside caller, and associate the merged second constituent audio data with the call center agent. The second data file 247 can be compressed before being stored in memory.

It is known in the art that “cradle-to-grave” recording may be used to record all information related to a particular telephone call from the time the call enters the contact center to the later of: the caller hanging up or the agent completing the transaction. All of the interactions during the call are recorded, including interaction with an IVR system, time spent on hold, data keyed through the caller's key pad, conversations with the agent, and screens displayed by the agent at his/her station during the transaction.

As shown in FIGS. 11-13, once the first and second constituent voice data are separated one from the other, each of the first and second constituent voice data can be independently mined and analyzed. It will be understood that “mining” as referenced herein is to be considered part of the process of analyzing the constituent voice data. It is also contemplated by the present invention that the mining and behavioral analysis be conducted on either or both of the constituent voice data.

Even with conventional audio mining technology, application of linguistic-based psychological behavioral models directly to an audio file can be very difficult. In particular, disparities in dialect, phonemes, accents and inflections can impede or render burdensome accurate identification of words. And while it is contemplated by the present invention that mining and analysis in accordance with the present invention can be applied directly to voice data configured in audio format, in a preferred embodiment of the present invention, the voice data to be mined and analyzed is first translated into a text file. It will be understood by those of skill that the translation of audio to text and subsequent data mining may be accomplished by systems known in the art. For example, the method of the present invention may employ software such as that sold under the brand name Audio Mining SDK by Scansoft, Inc., or any other audio mining software suitable for such applications.

As shown in FIGS. 11-13, the separated voice data is mined for behavioral signifiers associated with a linguistic-based psychological behavioral model. In particular, the method of the present invention searches for and identifies text-based keywords (i.e., behavioral signifiers) relevant to a predetermined psychological behavioral model.

According to a one embodiment of the present invention, the psychological behavioral model used to analyze the voice data is the Process Communication Model™ (“PCM”) developed by Dr. Taibi Kahler. PCM is a psychological behavioral analytic tool which presupposes that all people fall primarily into one of six basic personality types: Reactor, Workaholic, Persister, Dreamer, Rebel and Promoter. Although each person is one of these six types, all people have parts of all six types within them arranged like a six-tier configuration. Each of the six types learns differently, is motivated differently, communicates differently, and has a different sequence of negative behaviors they engage in when they are in distress. Importantly, according to PCM, each personality type of PCM responds positively or negatively to communications that include tones or messages commonly associated with another of the PCM personality types. Thus, an understanding of a communicant's PCM personality type offers guidance as to an appropriate responsive tone or message or wording.

According to the PCM Model the following behavioral characteristics are associated with the respective personality types:

PROCESS COMMUNICATION MODEL (PCM) BEHAVIORAL PERSONALITY TYPE CHARACTERISTICS Reactors compassionate, sensitive, and warm; great “people skills” and enjoy working with groups of people Workaholics responsible, logical, and organized Persisters conscientious, dedicated, and observant; tend to follow the rules and expect others to follow them Dreamers reflective, imaginative, and calm Rebels creative, spontaneous, and playful Promoters resourceful, adaptable, and charming

These behavioral characteristics may be categorized by words, tones, gestures, postures and facial expressions, can be observed objectively with significantly high interjudge reliability. According to one embodiment shown in FIG. 13, the present invention mines significant words within one or both of the separated first and second constituent voice data, and applies PCM to the identified words. For example, the following behavioral signifiers (i.e., words) may be associated with the corresponding behavioral type in the PCM Model:

PROCESS COMMUNICATION MODEL (PCM) BEHAVIORAL PERSONALITY TYPE SIGNIFIERS Reactors Emotional Words Workaholics Thought Words Persisters Opinion Words Dreamers Reflection Words Rebels Reaction Words Promoters Action Words

In another embodiment, the present method mines for such significant words within the merged second data file 247 described above, and apply PCM to the identified words. Alternatively, the first data file 241 can be mined for significant words. As shown in FIG. 13, when a behavioral signifier is identified within the voice data 62, the identified behavioral signifier is executed against a system database which maintains all of the data related to the psychological behavioral model 66. Based on the behavioral signifiers identified in the analyzed voice data, a predetermined algorithm 64 is used to decipher a linguistic pattern that corresponds to one or more of the PCM personality types 68. More specifically, the present method mines for linguistic indicators (words and phrases) that reveal the underlying personality characteristics of the speaker during periods of distress. Non-linguistic indicators may also be identified to augment or confirm the selection of a style for each segment of speech. Looking at all the speech segments in conjunction with personality information the software determines an order of personality components for the caller by weighing a number of factors such as timing, position, quantity and interaction between the parties in the dialog.

In one embodiment, the behavioral assessment data 55 includes sales effectiveness data. According to such an embodiment, the voice data is mined for linguist indicators to determine situations in which the call center agent made a sale or failed at an opportunity to make a sale. The failed opportunities may include failure to make an offer for a sale, making an offer and failure in completing the sale, or failure to make a cross-sale.

The resultant behavioral assessment data 55 is stored in a database so that it may subsequently be used to comparatively analyze against behavioral assessment data derived from analysis of the other of the first and second constituent voice data 56. The software considers the speech segment patterns of all parties in the dialog as a whole to refine the behavioral and distress assessment data of each party, making sure that the final distress and behavioral results are consistent with patterns that occur in human interaction. Alternatively, the raw behavioral assessment data 55 derived from the analysis of the single voice data may be used to evaluate qualities of a single communicant (e.g., the customer or agent behavioral type, etc.). The results generated by analyzing voice data through application of a psychological behavioral model to one or both of the first and second constituent voice data can be graphically illustrated as discussed in further detail below.

It should be noted that, although one preferred embodiment of the present invention uses PCM as a linguistic-based psychological behavioral model, it is contemplated that any known linguistic-based psychological behavioral model be employed without departing from the present invention. It is also contemplated that more than one linguistic-based psychological behavioral model be used to analyze one or both of the first and second constituent voice data.

In addition to the behavioral assessment of voice data, the method of the present invention may also employ distress analysis to voice data. As may be seen in FIG. 2, linguistic-based distress analysis is preferably conducted on both the textual translation of the voice data and the audio file containing voice data. Accordingly, linguistic-based analytic tools as well as non-linguistic analytic tools may be applied to the audio file. For example, one of skill in the art may apply spectral analysis to the audio file voice data while applying a word spotting analytical tool to the text file. Linguistic-based word spotting analysis and algorithms for identifying distress can be applied to the textual translation of the communication. Preferably, the resultant distress data is stored in a database for subsequent analysis of the communication.

As shown in FIG. 2, it is also often desirable to analyze non-linguistic phone events occurring during the course of a conversation such as hold times, transfers, “dead-air,” overtalk, etc. Accordingly, in one embodiment of the present invention, phone event data resulting from analysis of these non-linguistic events is generated. Preferably, the phone event data is generated by analyzing non-linguistic information from both the separated constituent voice data, or from the subsequently generated audio file containing at least some of the remerged audio data of the original audio waveform. It is also contemplated that the phone event data can be generated before the audio waveform is separated.

According to a preferred embodiment of the invention as shown in FIG. 13, both the first and second constituent voice data are mined and analyzed as discussed above 64, 66. The resulting behavioral assessment data 55, phone event data 70 and distress assessment data 72 from each of the analyzed first and second constituent voice data are comparatively analyzed in view of the parameters of the psychological behavioral model to provide an assessment of a given communication. From this comparative analysis, call assessment data relating to the totality of the call may be generated 56.

Generally, call assessment data is comprised of behavioral assessment data, phone event data and distress assessment data. The resultant call assessment data may be subsequently viewed to provide an objective assessment or rating of the quality, satisfaction or appropriateness of the interaction between an agent and a customer. In the instance in which the first and second constituent voice data are comparatively analyzed, the call assessment data may generate resultant data useful for characterizing the success of the interaction between a customer and an agent.

Thus, as shown in FIGS. 11 and 12, when a computer program is employed according to one embodiment of the present invention, a plurality of code segments are provided. The program comprises a code segment for receiving a digital electronic signal carrying an audio waveform 46. In accordance with the voice separation software described above, a code segment identifies a communication protocol associated with the telephonic signal 47. A code segment is also provided to separate first and second constituent voice data of the communication one from the other by recording the audio waveform in stereo format to a first electronic data file which has a first and second audio track 48. As discussed above, the first constituent voice data is automatically recorded on the first audio track based on the identified communication protocol, and the second constituent voice data is automatically recorded on the second audio track based on the identified communication protocol.

The software also includes a code segment for separately applying a non-linguistic based analytic tool to each of the separated first and second constituent voice data, and to generate phone event data corresponding to the analyzed voice data 50. A code segment translates each of the separated first and second constituent voice data into text format and stores the respective translated first and second constituent voice data in a first and second text file 52. A code segment analyzes the first and second text files by applying a predetermined linguistic-based psychological behavioral model thereto 54. The code segment generates either or both of behavioral assessment data and distress assessment data corresponding to each of the analyzed first and second constituent voice data 54.

A code segment is also provided for generating call assessment data 56. The call assessment data is resultant of the comparative analysis of the behavioral assessment data and distress assessment data corresponding to the analyzed first voice data and the behavioral assessment data and distress assessment data corresponding to the analyzed second voice data. A code segment then transmits an output of event data corresponding to at least one identifying indicia (e.g., call type, call time, agent, customer, etc.) 58. This event data is comprised of a call assessment data corresponding to at least one identifying indicia (e.g., a CSR name, a CSR center identifier, a customer, a customer type, a call type, etc.) and at least one predetermined time interval. Now will be described in detail the user interface for accessing and manipulating the event data of an analysis.

In one embodiment of the present invention shown in FIG. 13, the analysis of the constituent voice data includes the steps of: translating constituent voice data to be analyzed into a text format 60 and applying a predetermined linguistic-based psychological behavioral model to the translated constituent voice data. In applying the psychological behavioral model, the translated voice data is mined 62. In this way at least one of a plurality of behavioral signifiers associated with the psychological behavioral model is automatically identified in the translated voice data. When the behavioral signifiers are identified, the behavioral signifiers are automatically associated with at least one of a plurality of personality types 68 associated with the psychological behavioral model 64, 66. By applying appropriate algorithms behavioral assessment data corresponding to the analyzed constituent voice data is generated 55.

The method and system of the present invention is useful in improving the quality of customer interactions with agents and ultimately customer relationships. In use, a customer wishing to engage in a service call, a retention call or a sales will call into (or be called by) a contact center. When the call enters the contact center it will be routed by appropriate means to a call center agent. As the interaction transpires, the voice data will be recorded as described herein. Either contemporaneously with the interaction, or after the call interaction has concluded, the recorded voice data will be analyzed as described herein. The results of the analysis will generate call assessment data comprised of behavioral assessment data, distress assessment data and phone event data. This data may be subsequently used by a supervisor or trainer to evaluate or train an agent, or take other remedial action such as call back the customer, etc.

As indicated above, it is often desirable to train call center agents to improve the quality of customer interactions with agents. Thus, as shown in FIGS. 33-36, the present invention provides a method for training the call center agent by analyzing telephonic communications between the call center agent and the customer. In one embodiment, a plurality of the pre-recorded first communications between outside callers and a specific call center agent are identified based on an identifying criteria 601. The pre-recorded first communication can be one of the separated constituent voice data or the subsequently generated audio file containing at least some of the remerged audio waveform of the original audio waveform.

The pre-recorded first communications to be used in training the call center agent are identified by comparatively analyzing the identifying criteria in view of event data 602. The event data can include behavioral assessment data, phone event data, and/or distress assessment data of the communications. For example, the identifying criteria can be phone event data such as excessive hold/silence time (e.g., caller is placed on hold for greater than predetermined time—e.g., 90 seconds—or there is a period of silence greater than a predetermined amount time—e.g., 30 seconds) or long duration for call type (i.e., calls that are a predetermined percentage—e.g., 150%—over the average duration for a given call type). Additionally, the identifying criteria can be distress assessment data such as upset customer, unresolved issue or program dissatisfaction or another data associated with distress assessment data. It is contemplated that the system identify potential identifying criteria based on an analysis of the behavioral assessment data, phone event data, and/or distress assessment data of the communications.

From this comparative analysis, coaching assessment data is generated. The coaching assessment data relates to the identified pre-recorded first communications corresponding to the identifying criteria 604. For example, if the identifying criteria is excessive hold/silence time, the coaching assessment data includes pre-recorded first communications having excessive hold/silence time. The resulting coaching assessment data is stored in a database so that it subsequently can be used to evaluate and/or train the call center agent to improve performance in view of the identifying criteria. Thus, if the identifying criteria were excessive hold/silence time, the call center agent would be trained to reduce the amount of excessive hold/silence time calls.

The coaching assessment data can further include first performance data related to the overall performance of the call center agent with respect to the identifying criteria. The first performance data can be derived from an analysis of the identified prerecorded first communication with respect to all communications—i.e., identified pre-recorded first communication percentage (the percentage of identified pre-recorded first communications out of total number of communications) or identified pre-recorded communication (total number of identified pre-recorded first communications). A first performance score for each identified pre-recorded first communication may be generated by analyzing each identified pre-recorded first communication and the corresponding first performance data. A composite first performance score may be generated corresponding to the aggregate of the first performance scores of the plurality of identified pre-recorded first communications.

The coaching assessment data can be comparatively analyzed against a predetermined criteria value threshold to evaluate the call center agent's performance or against event data derived from a plurality of identified second pre-recorded communications to determine if training was effective 606. As discussed above, the threshold may be a predetermined criteria set by the call center, the customer, or other objective or subjective criteria. Alternatively, the threshold may set by the performance score.

In order to evaluate a call center agent, the coaching assessment data is comparatively analyzed against a predetermined identifying criteria value threshold. In one embodiment, the first performance data related to the identified pre-recorded first communication is comparatively analyzed with the predetermined identifying criteria value threshold 614. Based on the resultant comparative analysis, a notification is generated 616. For example, the percentage of excessive hold/silence calls in the pre-recorded first communications is compared with the identifying criteria value threshold. If the percentage of excessive hold/silence calls in the pre-recorded first communications is greater than the identifying criteria value threshold, the call center agent is underperforming and a notification is automatically generated 616.

In one embodiment, the coaching assessment data includes sales effectiveness data. The sales effectiveness data related to the identified pre-recorded first communications is comparatively analyzed against a predetermined identifying criteria value threshold. For example, the percentage of calls that the call center agent failed to make an offer for a cross-sale is compared with the identifying criteria value threshold. If the percentage of calls that the call center agent failed to make an offer for a cross-sale is greater than the identifying criteria value threshold, the call center agent is underperforming, and a notification is generated.

In another embodiment, the first performance score for each identified pre-recorded first communication is compared with the second performance score for the identifying criteria value threshold. In this case, if a predetermined number of first performance scores are less than (or greater than) the identifying criteria value threshold, a notification is generated. In another embodiment, the composite first performance score for the identified pre-recorded first communications is compared with the second performance score for the identifying criteria value threshold. If the first composite performance score is less than (or greater than) the second composite performance score, a notification is generated.

Preferably, the notification is an electronic communication, such as an email transmitted to a supervisor or trainer indicating that the call center agent is underperforming. The notification may be any other type of communication, such as a letter, a telephone call, or an automatically generated message on a website. The notification permits the supervisor or trainer to take remedial action, such as set up a training session for the call center agent. In one embodiment, the coaching assessment data related to an identifying criteria can be comparatively analyzed against the identifying criteria value threshold for a plurality call center agents. Based on the collective comparative analysis, a notification is generated if a predetermined number or percentage of call center agents are underperforming. In this manner, the trainer or supervisor is notified that multiple call center agents need to be trained with respect to the same criteria.

As noted above, the identifying criteria of the coaching assessment data can also be used to train a call center agent. In order to determine if the call center agent training was effective, the coaching assessment data can be comparatively analyzed against event data derived from a plurality of identified second pre-recorded communications. To determine if the training was effective, the second pre-recorded communications should have taken place after the call center agent training session. The pre-recorded second communications are identified according to the same identifying criteria used to identify the pre-recorded first communications in the coaching assessment data 608. Similar to the pre-recorded first communications, the pre-recorded second communications can be one of the separated constituent voiced data or the subsequently generated audio file containing at least some of the remerged audio waveform of the original audio waveform.

Second performance data related to the overall performance of the call center agent with respect to the pre-recorded second communications can be generated. As with the first performance data, the second performance data can be derived from an analysis of the identified pre-recorded second communication with respect to all communications—i.e., identified pre-recorded second communication percentage (the percentage of identified pre-recorded second communications out of total number of communications) or identified pre-recorded communication (total number of identified pre-recorded second communications). A second performance score for each identified pre-recorded second communication may be generated by analyzing each identified pre-recorded second communication and the corresponding second performance data. A composite second performance score may be generated corresponding to the aggregate second performance score for each of the plurality of identified pre-recorded second communications.

The second performance data related to the identified pre-recorded second communications is comparatively analyzed with the first performance data of the coaching assessment data 610. Based on the resultant comparative analysis, a notification is generated 612.

In one embodiment, the identified pre-recorded second communication percentage is compared with the identified pre-recorded first communication percentage. For example, the percentage of excessive hold/silence calls in the pre-recorded first communications that took place before the training session is compared with the percentage of excessive hold/silence calls in the pre-recorded second communications that took place after the training session 610. If the percentage of excessive hold/silence calls in the pre-recorded second communications is less than the percentage of excessive hold/silence calls in the pre-recorded first communications, the training session was successful. Conversely, if the percentage of excessive hold/silence calls in the pre-recorded second communications is greater than the percentage of excessive hold/silence calls in the pre-recorded first communications, the training session was unsuccessful and a notification is automatically generated 612.

In another embodiment, the first performance score for each identified pre-recorded first communication is compared with the second performance score for each identified pre-recorded second communication. In this case, if a predetermined number of second performance scores are less than (or greater than) a predetermined number of first performance scores, a notification is generated. In another embodiment, the composite first performance score for the identified pre-recorded first communications is compared with the composite second performance score for the identified pre-recorded second communications. If the second composite performance score is less than (or greater than) the first composite performance score, a notification is generated.

Preferably, the notification is an electronic communication, such as an email transmitted to a supervisor or trainer indicating that the training session for the call center agent was unsuccessful. The notification permits the supervisor or trainer to take remedial action, such as set up another training session for the call center agent. In one embodiment, the coaching assessment data related to an identifying criteria can be comparatively analyzed against event data derived from a plurality of identified second pre-recorded communications for a plurality of call center agents. Based on the collective comparative analysis, a notification is generated if a predetermined number or percentage of call center agents have unsuccessful training sessions. In this manner, the trainer or supervisor is notified that multiple call center agents need to be trained with respect to the same criteria.

Graphical and pictorial analysis of the call assessment data (and event data) is accessible through a portal by a subsequent user (e.g., a supervisor, training instructor or monitor) through a graphical user interface. A user of the system 1 described above interact with the system 1 via a unique graphical user interface (“GUI”) 400. The GUI 400 enables the user to navigate through the system 1 to obtain desired reports and information regarding the caller interaction events stored in memory. The GUI 400 can be part of a software program residing in whole or in part in the a computer 12, or it may reside in whole or in part on a server coupled to a computer 12 via a network connection, such as through the Internet or a local or wide area network (LAN or WAN). Moreover, a wireless connection can be used to link to the network.

In the embodiment shown in FIGS. 14-32, the system 1 is accessed via an Internet connection from a computer. Known browser technology on the computer can be implemented to reach a server hosting the system program. The GUI 400 for the system will appear as Internet web pages on the display of the computer.

As shown in FIG. 14, the GUI 400 initially provides the user with a portal or “Log On” page 402 that provides fields for input of a user name 404 and password 406 to gain access to the system. Additionally, the GUI 400 can direct a user to one or more pages for setting up a user name and password if one does not yet exist.

Referring to FIG. 15, once logged into the system 1, the user can navigate through the program by clicking one of the elements that visually appear as tabs generally on the upper portion of the display screen below any tool bars 408. In the embodiment shown in FIG. 15, the system 1 includes a PROFILES tab 410, a REVIEW tab 412, a METRICS tab 414 and a COACHING tab 620. A variety of the other tabs with additional information can also be made available.

The computer program associated with the present invention can be utilized to generate a large variety of reports relating to the recorded call interaction events, the statistical analysis of each event and the analysis of the event from the application of the psychological model. The GUI 400 is configured to facilitate a user's request for a specific report and to visually display the reports on the user's display.

The REVIEW tab 412 enables the user to locate one or more caller interaction events (a caller interaction event is also herein referred to as a “call”) stored in the memory. The REVIEW tab 412 includes visual date fields or links 416, 418 for inputting a “from” and “to” date range, respectively. Clicking on the links 416, 418 will call a pop-up calendar for selecting a date. A drop down menu or input field for entering the desired date can also be used.

The caller interaction events are divided into folders and listed by various categories. The folders can be identified or be sorted by the following event types: upset customer/issue unresolved; upset customer/issued resolved; program dissatisfaction; long hold/silence (e.g., caller is placed on hold for greater than a predetermined time—e.g., 90 seconds—or there is a period of silence greater than a predetermined amount of time—e.g., 30 seconds); early hold (i.e., customer is placed on hold within a predetermined amount of time—e.g., 30 seconds—of initiating a call); no authentication (i.e., the agent does not authorize or verify an account within a predetermined time—e.g., the first three minutes of the call); inappropriate response (e.g., the agent exhibits inappropriate language during the call); absent agent (i.e., incoming calls where the agent does not answer the call); long duration for call type (i.e., calls that are a predetermined percentage over—e.g., 150%—the average duration for a given call type); and transfers (i.e., calls that end in a transfer). The categories include: customers, CSR agents, and customer service events.

The REVIEW tab 412 includes a visual link to a customer's folder 420. This includes a list of calls subdivided by customer type. The customer folder 420 may include subfolders for corporate subsidiaries, specific promotional programs, or event types (i.e., upset customer/issue unresolved, etc.).

The REVIEW tab 412 also includes a visual link to call center or CSR agent folders 422. This includes a list of calls divided by call center or CSR agents. The initial breakdown is by location, followed by a list of managers, and then followed by the corresponding list of agents. The REVIEW tab 412 also includes a visual link to a customer service folders 424. This includes a list of calls subdivided by caller events, call center or CSR agent, and other relevant events.

The REVIEW tab 412 also includes a visual SEARCH link 426 to enable the user to search for calls based on a user-defined criteria. This includes the date range as discussed above. Additionally, the user can input certain call characteristics or identifying criteria. For example, the user can input a specific call ID number and click the SEARCH link 426. This returns only the desired call regardless of the date of the call. The user could choose an agent from a drop down menu or list of available agents. This returns all calls from the selected agent in the date range specified. The user could also choose a caller (again from a drop down menu or list of available callers). This returns all calls from the selected caller(s) within the date range.

The results from the search are visually depicted as a list of calls 428 as shown in FIG. 16. Clicking on any call 430 in the list 428 links the user to a call page 432 (as shown in FIG. 17) that provides call data and links to an audio file of the call which can be played on speakers connected to the user's computer.

The call page 432 also includes a conversation visual field 434 for displaying a time-based representation of characteristics of the call based on the psychological behavioral model. The call page 432 displays a progress bar 436 that illustrates call events marked with event data shown as, for example, colored points and colored line segments. A key 440 is provided explaining the color-coding.

The call page 432 further includes visual control elements for playing the recorded call. These include: BACK TO CALL LIST 442; PLAY 444; PAUSE 446; STOP 448; RELOAD 450; REFRESH DATA 452 and START/STOP/DURATION 454. The START/STOP/DURATION 454 reports the start, stop and duration of distinct call segments occurring in the call. The distinct call segments occur when there is a transition from a caller led conversation to an agent led conversation—or vice versa—and/or the nature of the discussion shifts to a different topic).

The REVIEW tab 412 also provides a visual statistics link 456 for displaying call statistics as shown in FIG. 18. The statistics can include information such as call duration, average duration for call type, caller talk time, number of holds over predetermined time periods (e.g., 90 seconds), number of silences, customer satisfaction score, etc.

The REVIEW tab 412 also provides a comments link 458. This will provide a supervisor with the ability to document comments for each call that can be used in follow-up discussions with the appropriate agent.

The METRICS tab 414 allows the user to generate and access Reports of caller interaction event information. The METRICS tab 414 includes two folders: a standard Reports folder 460 and an on-demand Reports folder. The standard reports folder 460 includes pre-defined call performance reports generated by the analytics engine for daily, weekly, monthly, quarterly, or annual time intervals. These Reports are organized around two key dimensions: caller satisfaction and agent performance. The on-demand reports folder 462 includes pre-defined call performance reports for any time interval based around two key dimensions: caller and agent.

The GUI 400 facilitates generating summary or detailed Reports as shown in FIG. 19. The user can select a Report time range via a pop-up calendar. For summary Reports, the user can select from: client satisfaction; summary by call type; and non-analyzed calls. For detailed Reports, the user can indicate the type of Report requested and click the Open Reports link 464. Additionally, the user can generate Program Reports. The user selects a client and filters the client by departments or divisions.

A CLIENT SATISFACTION REPORT 466 is shown in FIG. 20. The client satisfaction Report 466 is a summary level report that identifies analysis results by client for a specified time interval. The CLIENT SATISFACTION REPORT 466 contains a composite Satisfaction Score 468 that ranks relative call satisfaction across event filter criteria. The CLIENT SATISFACTION REPORT 466 is also available in pre-defined time intervals (for example, daily, weekly, monthly, quarterly, or annually).

The CLIENT SATISFACTION REPORT 466 includes a number of calls column 470 (total number of calls analyzed for the associated client during the specified reporting interval), an average duration column 472 (total analyzed talk time for all calls analyzed for the associated client divided by the total number of calls analyzed for the client), a greater than (“>”) 150% duration column 474 (percentage of calls for a client that exceed 150% of the average duration for all calls per call type), a greater than 90 second hold column 476 (percentage of calls for a client where the call center agent places the client on hold for greater than 90 seconds), a greater than 30 second silence column 478 (percentage of calls for a client where there is a period of continuous silence within a call greater than 30 seconds), a customer dissatisfaction column 480 (percentage of calls for a client where the caller exhibits dissatisfaction or distress—these calls are in the dissatisfied caller and upset caller/issue unresolved folders), a program dissatisfaction column 482 (percentage of calls where the caller exhibits dissatisfaction with the program), and a caller satisfaction column 484 (a composite score that represents overall caller satisfaction for all calls for the associated client).

The caller satisfaction column 484 is defined by a weighted percentage of the following criteria as shown in FIG. 21: >150% duration (weight 20%), >90 second hold (10%), >30 second silence (10%), caller distress (20%), and program dissatisfaction (20%). All weighted values are subtracted from a starting point of 100.

The user can generate a summary by CALL TYPE REPORT 486 as shown in FIG. 22. The CALL TYPE REPORTS 486 identify analysis results by call type for the specified interval. The summary by call type Report 486 contains a composite satisfaction score 488 that ranks relative client satisfaction across event filter criteria. The CALL TYPE REPORT 488 includes a call type column 490, as well as the other columns described above.

The user can generate a NON-ANALYZED CALLS REPORT 492 as shown in FIG. 23. The NON-ANALYZED CALLS REPORT 492 provides a summary level report that identifies non-analyzed calls for the specified time interval.

As shown in FIG. 24, the user can generate a DETAIL LEVEL REPORT 494. The detail level Report 494 identifies analysis results by client and call type for the specified time interval. The DETAIL LEVEL REPORT 494 contain a composite satisfaction score 496 that ranks relative client satisfaction for each call type across event filter criteria.

A PROGRAM REPORT 498 is shown in FIG. 25. This is a detail level report that identifies analysis results by client departments or divisions for the specified time interval. THE PROGRAM REPORT 498 contain a composite satisfaction score 500 that ranks relative client satisfaction for each call type across event filter criteria.

The user can also generate a number of CALL CENTER or CSR AGENT REPORTS. These include the following summary reports: corporate summary by location; CSR agent performance; and non-analyzed calls. Additionally, the user can generate team reports. The team Reports can be broken down by location, team or agent.

A CORPORATE SUMMARY BY LOCATION REPORT 502 is shown in FIG. 26. This detail level Report 502 identifies analysis results by location for the specified time interval, and contains a composite score that rank relative client performance for each call type across event filter criteria. The CORPORATE SUMMARY BY LOCATION REPORT 502 includes a location column 504 (this identifies the call center location that received the call), a number of calls column 506 (total number of calls received by the associated call center location during the specified reporting interval, an average duration column 508 (total analyzed talk time for all calls analyzed for the associated CSR agent divided by the total number of calls analyzed for the agent), a greater than 150% duration column 510 (percentage of calls for a CSR agent that exceed 150% of the average duration for all calls, a greater than 90 second hold column 512 (percentage of calls for a CSR agent where the CSR places the caller on hold for greater than 90 seconds), a greater than 30 second silence column 514 (percentage of calls for a CSR agent where there is a period of continuous silence within a call greater than 30 seconds), a call transfer column 516 (percentage of calls for a CSR agent that result in the caller being transferred), an inappropriate response column 518 (percentage of calls where the CSR agent exhibits inappropriate behavior or language), an appropriate response column 520 (percentage of calls where the CSR agent exhibits appropriate behavior or language that result in the dissipation of caller distress—these calls can be found in the upset caller/issue resolved folder), a no authentication column 522 (percentage of calls where the CSR agent does not authenticate the caller's identity to prevent fraud), and a score column 524 (a composite score that represents overall call center performance for all calls in the associated call center location.)

The values 526 in the score column 524 are based on the weighted criteria shown in FIG. 27. All weighted values are subtracted from a starting point of 100 except for “appropriate response,” which is an additive value.

A CSR PERFORMANCE REPORT 528 is shown in FIG. 28. This is a detail level report that identifies analysis results by CSR for the specified time interval. This Report 528 contains a composite score that ranks relative CSR performance for each call type across event filter criteria.

FIG. 29 shows a NON-ANALYZED CALLS REPORT 530. This is a detail level report that identifies analysis results by non-analyzed CSR calls for a specified time interval.

A LOCATION BY TEAM REPORT 532 is shown in FIG. 30. This is a summary level report that identifies analysis results by location and team for the specified time interval. This Report 532 contains a composite score that ranks relative CSR performance across event filter criteria by team.

FIG. 31 shows a TEAM BY AGENT REPORT 534. This is a summary level report that identifies analysis results by team and agent for the specified time interval. These Reports 534 contain a composite performance score that ranks relative CSR performance across event filter criteria by agent.

FIG. 32 shows a CSR BY CALL TYPE REPORT 536. This is detail level report that identifies analysis results by CSR and call type for the specified time interval. These Reports 536 contain a composite performance score that ranks relative CSR performance across event filter criteria by call type.

The COACHING tab 620 enables a user to locate one of more caller interaction events to evaluate and train call center agents to improve the quality of customer interactions with the agents. The COACHING tab 620 includes visual date fields 622, 624 for inputting a “from” and “to date”, respectively. Clicking on the links 416, 418 will call a pop-up calendar for selecting a date. A drop down menu or input field for entering the desired date can also be used.

The COACHING tab 620 displays caller interaction event information. The caller interaction event information includes check boxes for selecting the caller interaction event information as the identifying criteria 626. Based on the selection of the identifying criteria 626, a plurality of pre-recorded first communications between outside caller and a specific call center agent are identified 628. Information relating to the identified criteria is also displayed 630. A value may be entered in visual call field 632 to specify the number of pre-identified calls to display.

The COACHING tab 620 includes a coaching page 634 to train the call center agent to improve performance in view of the identifying criteria, as illustrated in FIG. 35. The coaching page 634 displays a progress bar 636 that illustrates call events marked with event data shown as, for example, colored points and colored line segments. The coaching page 634 includes a comment box 640 for the call agent to indicate areas to be trained. Comments from others may also be displayed. The coaching page 634 includes a check-box 638 for requesting additional training on the selected identifying criteria.

Referring to FIG. 36, the COACHING tab 620 includes a graphical representation 642 of the number of calls that are identified based on the identifying criteria 644. In one embodiment, the graphical representation displays the percentage of calls identified based on the identifying criteria 644 for each week during an identified time period. In this manner, it can be determined if the training session for the call center agent was successful.

In a still further embodiment of the present invention, a method and system for searching and selecting and/or navigating to specific telephone communications is provided. Specifically, stored communications may be searched based on metrics, or conditions, of interest. For example, a user may provide search criteria to the computer program of the present invention, and all telephone communications that satisfy the search criteria may be listed and displayed for the user to select. Alternatively, a specific number of communication that are less than the total number of communications may be listed and displayed for selection by the user.

In one embodiment, a method for searching and selecting and/or navigating to specific recorded communications is provided. As stated above, communications between a customer and a contact center may be digitized and analyzed, and assessment data is assigned to the communication. For example, agent performance information may be assigned to the digitized communication indicating specific information relating to how the contact center agent performed on the communication. Other data assigned to the communication may relate to customer satisfaction information, business process improvement information, or any other information created after analysis of the communication using the methods and systems provided herein.

In a first step of this embodiment, all communications, typically telephonic communications, may be recorded over a given time period. For example, the time period may be any time period, such as minutes, hours, days, etc., and assessment data may be assigned to each communication using the analysis systems and methods of the present invention as described above. Of that set of communications that are recorded over a specified time period, a number of communications may be culled based on high level criteria, in a second step of this embodiment. In a third step of this embodiment, the communications may be selected based on their categorization according to the present invention. For example, communications based on whether they relate to “service coaching” may be cut from the set of communications over the given time period. These communications may related to how the agent provided service to customers, such as whether the call was designated as too long, or whether the agent was given a low agent score based on analysis of the communication. In addition, communications based on whether they relate to “sales coaching” may be culled from the set of communications over the given time period. These communications may relate to whether the communication related to a sales opportunity.

Further, communications based on whether they related to “collections coaching” may be culled from the set of communications over the given time period. These communications may relate to whether the communication related to a collections opportunity. In addition, communications based on “voice of the customer” may be culled from the set of communications over the given time period. These communications may relate to whether the communication related to customer satisfaction or dissatisfaction. Of course, any high level criteria may be established and utilized to cull the set of communications from a given time period, and the invention should not be limited as herein described.

After the first cut of communications in the third step, the computer program may proceed under a “manual path” or an “automatic path”. If the manual path is designated or otherwise selected by a user, a graphical user interface (GUI) may be displayed to select conditions or metrics of interest in the culled communications from the given time period in a fourth step. Examples of GUIs useful for the present embodiment of the invention described herein, and further described below. In yet a fifth step, a user may select one or more conditional metrics that may be utilized to select communications that match the one or more conditional metrics. For example, a user may wish to find communications based on whether the call was inbound or outbound, whether the customer had previously contacted the company before, the gender of the customer, or other criteria that may be utilized to find and display communications having characteristics or assessment data matching the criteria selected.

If the “automatic path” is selected by the user, than previously determined or programmed metric conditions or criteria may be applied against the communications culled from the given time period via an automated criteria selection step.

Whether the “manual” path or the “automatic” path is chosen, the computer program of the present embodiment described herein may then display a list of communications that meet or otherwise match with the specified criterion or criteria designated by the user. The list may be displayed in the GUI, whereupon the user may then select one or more of the communications, and navigate to the specific communication examples for playback or for further analysis.

In another embodiment related to the logic, the system described herein may utilize “best fit” logic to search for and list communications that match the specified criterion or criteria. Specifically, a method is provided for determining the “best fit” of communications to specified criteria or metrics. In a first step, a “bit map” for each communication is constructed to specify which metric conditions or criteria are met. For example, there may be nine metric conditions or criteria utilized to determine matches of communications. The “bit map” may simply be a string of bits or logic characters, designating whether the communication matches the specific metric conditions or criteria designated. If all nine metric conditions or criteria are met, then each bit within the bit map may have a positive or “1” designation. Alternatively, if no metric conditions or criteria are met by the communication, each bit within the bit map may be designated with a negative or “0” designation. If, for example, the first six metric conditions or criteria match the communication, but the last three do not, then the bit map may have 6 positive or “1” bits followed by 3 negative or “0” bits.

The bit map may then be utilized to determine the communications that best fit the metric conditions or criteria. Specifically, the computer program may then build a result set based on all calls that have the appropriate “bit map” signature. All communications in the result set may be provided in the result set, or only those communications having a certain number of positive or “1” bits may be ordered. For example, a user may wish to only include those communications with an “all true” result (i.e., the bit map consists of all “1s” in the bit map. Alternatively, the user may decide to build a set of communications having 8 “1s”.

After the computer program builds the result set, the result set is then ordered from “best to worst.” The order is provided with the communications having the best fit provided first and the worst fit provided last. In the event of a bit set “tie” (where the bit set has the same number of positive results or 1s, each bit may be weighted so that certain positive bits cause the communication to rank higher than other communications with the same number of positive bits, but for different metric conditions or criteria. Alternatively, communications having the same number of positive bits may be ordered randomly, or by date, or by any other condition apparent to one having ordinary skill in the art.

The user may further designate or select a specific number of results to display, whereupon the list is cutoff at the specified number of examples. The list is then displayed to the user via the GUI. For example, the user may wish to have displayed only one communication that best matches the metric conditions or criteria applied thereto. The communication having the best bit map is then displayed for the user to select for viewing assessment data assigned thereto, for listening to or otherwise further analyzing.

In yet another embodiment, an exemplary sample GUI can illustrate a set of metric conditions or criteria that may be selected and applied to find matching communications. In this example, a time period is selected by inputting a date into a FROM field and a date into a TO field, thereby designating a time period. A first cut of calls has been done by user selected high-level criteria, with the user selecting “Voice of the People” under calls relating to “Customer Service.” In this exemplary GUI embodiment, the computer program has displayed on the GUI that within that time period, the “agents received 1470 calls of which 29% had customer dissatisfaction” and proceeds to list user-selectable characteristics. Any one, some or all of these characteristics provided in the list of user-selectable characteristics may then be selected by the user, and specified to provide metric conditions or criteria for searching for communications that match the selected and specified metric conditions or criteria. In the GUI example, one metric condition or criterion is selected, “Had the following personalities: 25% Thoughts.” The number of calls is designated as “1” by the user, so the list generated by the computer program and displayed on the GUI displays only one result, which result has the best fit for matching the selected metric condition or criterion. The displayed result in the list generated by the computer program based on the metric condition or criterion selected by the user may then be selected for viewing assessment data assigned thereto, listening thereto, or otherwise analyzing the same.

While the specific embodiments have been illustrated and described, numerous modifications come to mind without significantly departing from the spirit of the invention, and the scope of protection is only limited by the scope of the accompanying claims. 

What is claimed is:
 1. An interface portal system, which comprises: a non-transitory computer readable medium comprising a plurality of instructions stored therein adapted to generate a coaching portal based on behavioral assessment data, the plurality of instructions comprising: instructions that, when executed, analyze one or more communications between a customer and an agent, wherein the analysis comprises instructions that, when executed, apply a linguistic-based psychological behavioral model to separated voice data for the customer, the agent, or both, from each communication by analyzing behavioral characteristics of the customer, the agent, or both, based on the one or more communications; instructions that, when executed, identify one or more customer-agent interaction events based on the analyzed behavioral characteristics; and instructions that, when executed, display a time-based graphic representation including a plurality of the identified customer-agent interaction events across a selected time interval based on one or more communications.
 2. The interface portal system of claim 1, which further comprises instructions that, when executed, automatically generate a coaching performance score for the agent across the selected time interval.
 3. The interface portal system of claim 1, which further comprises instructions that, when executed, automatically generate one or more coaching recommendations based on the identified one or more customer-agent interaction events for an agent.
 4. The interface portal system of claim 3, which further comprises instructions that, when executed, deliver the one or more coaching recommendations to the agent and compare a performance score for the agent over the selected time interval against a second performance score for the agent over a second selected time interval after the one or more coaching recommendations are delivered to the agent.
 5. The interface portal system of claim 4, wherein the graphic representation displayed includes a call statistics visual field selectable by a user to display statistics pertaining to the one or more customer-agent interaction events.
 6. The interface portal system of claim 1, wherein the call statistics visual field comprises one or more of: communication duration, average communication duration by call type, customer talk time, agent talk time, customer satisfaction score, number of holds over a pre-determined time period, hold time, number of silences longer than a predetermined time period, or a combination thereof.
 7. The interface portal system of claim 4, which further comprises instructions that, when executed, automatically generate and deliver a notification to an agent trainer, an agent supervisor, or both.
 8. The interface portal system of claim 7, wherein the notification relates to a plurality of agents regarding a need for training the plurality of agents regarding a particular criteria.
 9. The interface portal system of claim 1, wherein the selected time interval includes a day, a week, a month, a quarter or a year.
 10. The interface portal system of claim 1, wherein the linguistic-based psychological behavioral model comprises a six-tier configuration of six basic personality types.
 11. The interface portal system of claim 1, wherein the communication comprises at least one of a phone call, e-mail including voice data, web interaction including voice data, VoIP, or video including voice data.
 12. A method of providing coaching assessment based on behavioral assessment data across one or more recorded communications, which comprises: analyzing, by a server, one or more communications between a customer and an agent, wherein the analysis comprises instructions that, when executed, apply a linguistic-based psychological behavioral model to separated voice data for the customer, the agent, or both, from each communication by analyzing behavioral characteristics of the customer, the agent, or both, based on the one or more communications; identifying one or more customer-agent interaction events based on the analyzed behavioral characteristics; and displaying a time-based graphic representation including a plurality of the identified customer-agent interaction events across a selected time interval based on one or more communications.
 13. The method of claim 12, which further comprises automatically generating a coaching performance score for the agent across the selected time interval.
 14. The method of claim 12, which further comprises automatically generating one or more coaching recommendations based on the identified one or more customer-agent interaction events for an agent.
 15. The method of claim 14, which further comprises delivering the one or more coaching recommendations to the agent, and comparing a performance score for the agent over the selected time interval against a second performance score for the agent over a second selected time interval after the one or more coaching recommendations are delivered to the agent.
 16. The method of claim 12, wherein the displaying a time-based graphic representation comprises displaying a call statistics visual field selectable by a user to display statistics pertaining to the one or more customer-agent interaction events.
 17. The method of claim 16, wherein the call statistics visual field is selected to comprise one or more of: communication duration, average communication duration by call type, customer talk time, agent talk time, customer satisfaction score, number of holds over a pre-determined time period, hold time, number of silences longer than a predetermined time period, or a combination thereof.
 18. The method of claim 15, which further comprises automatically generating and delivering a notification to an agent trainer, an agent supervisor, or both.
 19. The method of claim 18, wherein the notification relates to a plurality of agents regarding a need for training the plurality of agents regarding a particular criteria.
 20. The method of claim 12, wherein the selected time interval is a day, a week, a month, a quarter or a year.
 21. The method of claim 12, wherein the linguistic-based psychological behavioral model is selected to comprise a six-tier configuration of six basic personality types.
 22. The method of claim 12, wherein the communication is selected to comprise at least one of a phone call, e-mail including voice data, web interaction including voice data, VoIP, or video including voice data. 